My HealtheVet (www.myhealth.va.gov) is a Personal Health Record portal designed to improve the delivery of health care services to Veterans, to promote health and wellness, and to engage Veterans as more active participants in their health care. The My HealtheVet portal enables Veterans to create and maintain a web-based PHR that provides access to patient health education information and resources, a comprehensive personal health journal, and electronic services such as online VA prescription refill requests and Secure Messaging. Veterans can visit the My HealtheVet website and self-register to create an account, although registration is not required to view the professionally-sponsored health education resources, including topics of special interest to the Veteran population. Once registered, Veterans can create a customized PHR that is accessible from any computer with Internet access.

These data are modelled using the OMOP Common Data Model v5.3.Correlated Data SourceNG tube vocabulariesGeneration RulesThe patient’s age should be between 18 and 100 at the moment of the visit.Ethnicity data is using 2021 census data in England and Wales (Census in England and Wales 2021) .Gender is equally distributed between Male and Female (50% each).Every person in the record has a link in procedure_occurrence with the concept “Checking the position of nasogastric tube using X-ray”2% of person records have a link in procedure_occurrence with the concept of “Plain chest X-ray”60% of visit_occurrence has visit concept “Inpatient Visit”, while 40% have “Emergency Room Visit”NotesVersion 0Generated by man-made rule/story generatorStructural correct, all tables linked with the relationshipWe used national ethnicity data to generate a realistic distribution (see below)2011 Race Census figure in England and WalesEthnic Group : Population(%)Asian or Asian British: Bangladeshi - 1.1Asian or Asian British: Chinese - 0.7Asian or Asian British: Indian - 3.1Asian or Asian British: Pakistani - 2.7Asian or Asian British: any other Asian background -1.6Black or African or Caribbean or Black British: African - 2.5Black or African or Caribbean or Black British: Caribbean - 1Black or African or Caribbean or Black British: other Black or African or Caribbean background - 0.5Mixed multiple ethnic groups: White and Asian - 0.8Mixed multiple ethnic groups: White and Black African - 0.4Mixed multiple ethnic groups: White and Black Caribbean - 0.9Mixed multiple ethnic groups: any other Mixed or multiple ethnic background - 0.8White: English or Welsh or Scottish or Northern Irish or British - 74.4White: Irish - 0.9White: Gypsy or Irish Traveller - 0.1White: any other White background - 6.4Other ethnic group: any other ethnic group - 1.6Other ethnic group: Arab - 0.6

Electronic Health Records Market Outlook

According to our latest research, the global Electronic Health Records (EHR) market size stood at USD 34.9 billion in 2024, reflecting robust adoption across healthcare systems worldwide. The market is anticipated to progress at a CAGR of 7.3% from 2025 to 2033, reaching an estimated USD 66.1 billion by 2033. This growth is primarily driven by the increasing demand for digital solutions to streamline healthcare delivery, rising government initiatives for health IT infrastructure, and the expanding need for data-driven patient care management.

One of the central growth factors for the Electronic Health Records market is the global push towards digital transformation in healthcare. As healthcare providers strive to improve patient outcomes and operational efficiency, EHR systems have become indispensable for storing, accessing, and analyzing patient data. The integration of advanced technologies such as artificial intelligence, machine learning, and interoperability standards has further accelerated EHR adoption. Governments in developed economies continue to mandate EHR usage, incentivizing providers through funding and regulatory frameworks, which in turn boosts the market’s expansion. Moreover, the COVID-19 pandemic underscored the importance of accessible digital records, further reinforcing the necessity of robust EHR systems.

Another significant driver of the EHR market is the increasing prevalence of chronic diseases and the aging global population. As the number of patients requiring long-term and coordinated care rises, healthcare providers are leveraging EHR solutions to enhance care coordination, reduce medical errors, and ensure continuity of care. The ability to share patient information seamlessly across different care settings is especially vital for managing complex cases. Additionally, the growing focus on value-based care and patient-centric models has led to higher investments in EHR platforms, which facilitate comprehensive data analytics, population health management, and personalized treatment plans.

Furthermore, the rapid proliferation of cloud computing and mobile health technologies is reshaping the Electronic Health Records market. Cloud-based EHR solutions offer scalability, cost-effectiveness, and remote accessibility, making them particularly attractive to small and medium-sized healthcare providers. These solutions enable real-time data sharing, telemedicine integration, and disaster recovery capabilities, all of which are crucial in today’s dynamic healthcare landscape. The shift towards interoperable and user-friendly EHR platforms is also fostering innovation, with vendors introducing customizable solutions tailored to the unique needs of various healthcare settings.

Regionally, North America continues to dominate the Electronic Health Records market, accounting for the largest share in 2024 due to the presence of advanced healthcare infrastructure, favorable government policies, and high EHR adoption rates. However, the Asia Pacific region is poised for the fastest growth, driven by rapid digitalization, increasing healthcare investments, and supportive regulatory initiatives. Europe follows closely, with strong emphasis on data privacy and cross-border health data exchange. Emerging markets in Latin America and the Middle East & Africa are also witnessing increased EHR adoption, albeit at a slower pace due to infrastructural and regulatory challenges.

Product Analysis

The Electronic Health Records market is segmented by product into On-Premise EHR and Cloud-Based EHR, each offering distinct advantages and challenges. On-premise EHR solutions, traditionally favored by large hospitals and healthcare networks, provide organizations with direct control over data security and system customization. These systems are typically installed and maintained within the healthcare provider’s own IT infrastructure, ensuring compliance with stringent regulatory r

A 100-patient database that contains in total 100 virtual patients, 372 admissions, and 111,483 lab observations.

Electronic Health Records (EHR) Software Market Outlook

The global Electronic Health Records (EHR) Software market size is poised for substantial growth, projected to expand from USD 32 billion in 2023 to USD 52 billion by 2032, reflecting a CAGR of approximately 5.2% during the forecast period. Growth in this market is primarily driven by increased adoption of healthcare IT solutions, the necessity for coordinated care, and the rising demand for an efficient healthcare system that allows for seamless information sharing across various medical platforms. EHR software plays a pivotal role in modernizing and streamlining clinical operations, significantly reducing the burden of paperwork while enhancing patient care quality and safety.

One of the major growth factors influencing the EHR software market is the increasing shift towards digitization in the healthcare sector. As governments and healthcare providers recognize the need for streamlined, efficient record-keeping processes, investments in EHR systems have grown exponentially. This shift is driven not only by the need to reduce administrative burdens but also by the push to deliver more personalized patient care. The implementation of EHR systems allows for improved data accuracy, real-time patient data access, and the facilitation of informed clinical decisions, all of which are crucial in enhancing the overall quality of healthcare services.

Another significant growth driver is the growing emphasis on regulatory compliance and government initiatives pushing for electronic health record adoption. In regions such as North America and Europe, legislation and policies like the Health Information Technology for Economic and Clinical Health (HITECH) Act and the General Data Protection Regulation (GDPR) have been pivotal. These regulations mandate and encourage healthcare facilities to adopt digital record-keeping practices, providing financial incentives and frameworks that further fuel the adoption of EHR systems. Such governmental support is critical as it not only ensures compliance but also inspires confidence among healthcare providers to transition from traditional paper-based records to advanced electronic systems.

The rising prevalence of chronic diseases and the subsequent increase in patient data generation are also significant contributors to market growth. Chronic conditions require continuous monitoring and long-term management, necessitating detailed and accurate patient records. EHR systems are invaluable in managing such vast amounts of data, enabling healthcare providers to efficiently track patient history, medication, and treatment plans. This capability is particularly important in enhancing patient outcomes and optimizing healthcare delivery, making EHR software indispensable in modern medical practices.

Community Health Systems EHR is a notable example of how electronic health records are being leveraged to enhance healthcare delivery. By integrating advanced EHR solutions, Community Health Systems has been able to streamline patient data management, improve clinical workflows, and facilitate better communication among healthcare providers. This integration not only enhances the quality of care but also supports the organization's commitment to patient safety and regulatory compliance. The adoption of such comprehensive EHR systems is crucial in addressing the challenges of modern healthcare, where the efficient handling of vast amounts of patient data is essential for optimal outcomes. As more healthcare organizations follow suit, the role of EHR systems in transforming healthcare delivery continues to expand.

Regionally, North America dominates the EHR software market due to its advanced healthcare infrastructure and early adoption of digital health solutions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This growth is attributed to rapidly developing healthcare infrastructures, increasing government initiatives to promote healthcare digitization, and an expanding geriatric population, which collectively drive the demand for efficient healthcare solutions. The increasing investment in healthcare IT infrastructure and the growing awareness of the benefits of EHRs among healthcare providers in the region are also key factors contributing to market expansion.

Product Type Analysis

The EHR software market is broadly segmented by product type into Cloud-Based and On-Premises solutions, each offering di

Electronic Medical Records Market Outlook

As per our latest research, the global Electronic Medical Records (EMR) market size reached USD 34.8 billion in 2024, reflecting robust adoption across healthcare systems worldwide. The market is poised for significant expansion with a projected CAGR of 7.3% from 2025 to 2033. By the end of 2033, the EMR market is forecasted to attain a value of approximately USD 65.8 billion. This impressive growth trajectory is primarily driven by the increasing digitalization of healthcare records, the need for improved patient care, and regulatory mandates for electronic data management in healthcare settings.

One of the most crucial growth factors propelling the Electronic Medical Records market is the global push towards healthcare modernization and interoperability. Governments and healthcare organizations are heavily investing in digital infrastructure to streamline patient data management and enhance care coordination. Initiatives such as the United States’ Health Information Technology for Economic and Clinical Health (HITECH) Act and similar policies in Europe and Asia Pacific have accelerated the adoption of EMR systems. These regulations not only incentivize healthcare providers to adopt electronic records but also impose penalties for non-compliance, further fueling market expansion. The growing emphasis on patient-centric care, reduction of medical errors, and the need for real-time access to patient information are compelling hospitals and clinics to transition from paper-based to electronic systems.

Another significant driver is the rapid advancement and integration of cutting-edge technologies within EMR platforms. Artificial Intelligence (AI), machine learning, and cloud computing are revolutionizing how patient data is captured, stored, and analyzed. These technologies are enabling predictive analytics, personalized medicine, and seamless data sharing across healthcare networks. The integration of telemedicine and remote patient monitoring solutions with EMR systems has also gained momentum, especially post-pandemic, as healthcare providers seek to offer virtual care without compromising on the quality or security of patient data. This technological evolution is not only enhancing the efficiency of healthcare delivery but is also making EMR solutions more scalable, secure, and user-friendly.

Furthermore, the rising prevalence of chronic diseases and the aging global population are contributing to the growing demand for comprehensive and accessible patient records. Chronic disease management requires continuous monitoring and long-term care coordination, both of which are facilitated by robust EMR systems. The ability to track patient histories, medication adherence, and clinical outcomes over time is invaluable for healthcare providers aiming to deliver value-based care. Additionally, the growing need for data-driven decision-making in healthcare, driven by the shift towards outcomes-based reimbursement models, is further accelerating the adoption of EMR platforms. These trends collectively underscore the critical role of EMRs in shaping the future of global healthcare delivery.

Regionally, North America continues to dominate the Electronic Medical Records market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The United States remains at the forefront due to its advanced healthcare infrastructure, favorable government policies, and high adoption rate of digital health technologies. Europe is experiencing steady growth, propelled by stringent data protection regulations and increasing investments in healthcare IT. Meanwhile, the Asia Pacific region is emerging as a lucrative market, driven by expanding healthcare access, government-led digital health initiatives, and a burgeoning patient population. Latin America and Middle East & Africa are witnessing gradual adoption, supported by efforts to modernize healthcare systems and improve patient outcomes.

Component Analysis

The Electronic Medical Records market is segmented by component

Get premium quality off-the-shelf EHR dataset to develop better performing machine learning models. Speak to our experts for Electronic Health Records data needs.

These data include electronic health records of a random sample of patients at the University of North Carolina healthcare system. In addition, we linked these data to results of hybrid air pollution models generated by a team at Harvard University. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Enquiries regarding access to electronic health records data can be submitted at https://tracs.unc.edu/. Format: These data include electronic medical records, which include sensitive information that cannot be released. In addition, we included results of propietary air pollution models generated by our colleagues at Harvard University. This dataset is associated with the following publication: Dillon, D., C. Ward-Caviness, A. Kshirsagar, J. Moyer, J. Schwartz, Q. Di, and A. Weaver. Associations between long-term exposure to air pollution and kidney function utilizing electronic healthcare records: a cross-sectional study. ENVIRONMENTAL HEALTH. Academic Press Incorporated, Orlando, FL, USA, 23(43): 1322, (2024).

Objective: To develop a clinical informatics pipeline designed to capture large-scale structured EHR data for a national patient registry.

Materials and Methods: The EHR-R-REDCap pipeline is implemented using R-statistical software to remap and import structured EHR data into the REDCap-based multi-institutional Merkel Cell Carcinoma (MCC) Patient Registry using an adaptable data dictionary.

Results: Clinical laboratory data were extracted from EPIC Clarity across several participating institutions. Labs were transformed, remapped and imported into the MCC registry using the EHR labs abstraction (eLAB) pipeline. Forty-nine clinical tests encompassing 482,450 results were imported into the registry for 1,109 enrolled MCC patients. Data-quality assessment revealed highly accurate, valid labs. Univariate modeling was performed for labs at baseline on overall survival (N=176) using this clinical informatics pipeline.

Conclusion: We demonstrate feasibility of the facile eLAB workflow. EHR data is successfully transformed, and bulk-loaded/imported into a REDCap-based national registry to execute real-world data analysis and interoperability.

Methods eLAB Development and Source Code (R statistical software):

eLAB is written in R (version 4.0.3), and utilizes the following packages for processing: DescTools, REDCapR, reshape2, splitstackshape, readxl, survival, survminer, and tidyverse. Source code for eLAB can be downloaded directly (https://github.com/TheMillerLab/eLAB).

eLAB reformats EHR data abstracted for an identified population of patients (e.g. medical record numbers (MRN)/name list) under an Institutional Review Board (IRB)-approved protocol. The MCCPR does not host MRNs/names and eLAB converts these to MCCPR assigned record identification numbers (record_id) before import for de-identification.

Functions were written to remap EHR bulk lab data pulls/queries from several sources including Clarity/Crystal reports or institutional EDW including Research Patient Data Registry (RPDR) at MGB. The input, a csv/delimited file of labs for user-defined patients, may vary. Thus, users may need to adapt the initial data wrangling script based on the data input format. However, the downstream transformation, code-lab lookup tables, outcomes analysis, and LOINC remapping are standard for use with the provided REDCap Data Dictionary, DataDictionary_eLAB.csv. The available R-markdown ((https://github.com/TheMillerLab/eLAB) provides suggestions and instructions on where or when upfront script modifications may be necessary to accommodate input variability.

The eLAB pipeline takes several inputs. For example, the input for use with the ‘ehr_format(dt)’ single-line command is non-tabular data assigned as R object ‘dt’ with 4 columns: 1) Patient Name (MRN), 2) Collection Date, 3) Collection Time, and 4) Lab Results wherein several lab panels are in one data frame cell. A mock dataset in this ‘untidy-format’ is provided for demonstration purposes (https://github.com/TheMillerLab/eLAB).

Bulk lab data pulls often result in subtypes of the same lab. For example, potassium labs are reported as “Potassium,” “Potassium-External,” “Potassium(POC),” “Potassium,whole-bld,” “Potassium-Level-External,” “Potassium,venous,” and “Potassium-whole-bld/plasma.” eLAB utilizes a key-value lookup table with ~300 lab subtypes for remapping labs to the Data Dictionary (DD) code. eLAB reformats/accepts only those lab units pre-defined by the registry DD. The lab lookup table is provided for direct use or may be re-configured/updated to meet end-user specifications. eLAB is designed to remap, transform, and filter/adjust value units of semi-structured/structured bulk laboratory values data pulls from the EHR to align with the pre-defined code of the DD.

Data Dictionary (DD)

EHR clinical laboratory data is captured in REDCap using the ‘Labs’ repeating instrument (Supplemental Figures 1-2). The DD is provided for use by researchers at REDCap-participating institutions and is optimized to accommodate the same lab-type captured more than once on the same day for the same patient. The instrument captures 35 clinical lab types. The DD serves several major purposes in the eLAB pipeline. First, it defines every lab type of interest and associated lab unit of interest with a set field/variable name. It also restricts/defines the type of data allowed for entry for each data field, such as a string or numerics. The DD is uploaded into REDCap by every participating site/collaborator and ensures each site collects and codes the data the same way. Automation pipelines, such as eLAB, are designed to remap/clean and reformat data/units utilizing key-value look-up tables that filter and select only the labs/units of interest. eLAB ensures the data pulled from the EHR contains the correct unit and format pre-configured by the DD. The use of the same DD at every participating site ensures that the data field code, format, and relationships in the database are uniform across each site to allow for the simple aggregation of the multi-site data. For example, since every site in the MCCPR uses the same DD, aggregation is efficient and different site csv files are simply combined.

Study Cohort

This study was approved by the MGB IRB. Search of the EHR was performed to identify patients diagnosed with MCC between 1975-2021 (N=1,109) for inclusion in the MCCPR. Subjects diagnosed with primary cutaneous MCC between 2016-2019 (N= 176) were included in the test cohort for exploratory studies of lab result associations with overall survival (OS) using eLAB.

Statistical Analysis

OS is defined as the time from date of MCC diagnosis to date of death. Data was censored at the date of the last follow-up visit if no death event occurred. Univariable Cox proportional hazard modeling was performed among all lab predictors. Due to the hypothesis-generating nature of the work, p-values were exploratory and Bonferroni corrections were not applied.

Introduction: Type 2 diabetes (T2D) is a multifactorial complex chronic disease with a high prevalence worldwide, and Type 2 diabetes patients with different comorbidities often present multiple phenotypes in the clinic. Thus, there is a pressing need to improve understanding of the complexity of the clinical Type 2 diabetes population to help identify more accurate disease subtypes for personalized treatment.Methods: Here, utilizing the traditional Chinese medicine (TCM) clinical electronic medical records (EMRs) of 2137 Type 2 diabetes inpatients, we followed a heterogeneous medical record network (HEMnet) framework to construct heterogeneous medical record networks by integrating the clinical features from the electronic medical records, molecular interaction networks and domain knowledge.Results: Of the 2137 Type 2 diabetes patients, 1347 were male (63.03%), and 790 were female (36.97%). Using the HEMnet method, we obtained eight non-overlapping patient subgroups. For example, in H3, Poria, Astragali Radix, Glycyrrhizae Radix et Rhizoma, Cinnamomi Ramulus, and Liriopes Radix were identified as significant botanical drugs. Cardiovascular diseases (CVDs) were found to be significant comorbidities. Furthermore, enrichment analysis showed that there were six overlapping pathways and eight overlapping Gene Ontology terms among the herbs, comorbidities, and Type 2 diabetes in H3.Discussion: Our results demonstrate that identification of the Type 2 diabetes subgroup based on the HEMnet method can provide important guidance for the clinical use of herbal prescriptions and that this method can be used for other complex diseases.

The dataset is Electronic Health Record Predicting collected from a private Hospital in Indonesia. It contains the patients laboratory test results used to determine next patient treatment whether in care or out care patient. The task embedded to the dataset is classification prediction.

Background:
The Millennium Cohort Study (MCS) is a large-scale, multi-purpose longitudinal dataset providing information about babies born at the beginning of the 21st century, their progress through life, and the families who are bringing them up, for the four countries of the United Kingdom. The original objectives of the first MCS survey, as laid down in the proposal to the Economic and Social Research Council (ESRC) in March 2000, were:

• to chart the initial conditions of social, economic and health advantages and disadvantages facing children born at the start of the 21st century, capturing information that the research community of the future will require
• to provide a basis for comparing patterns of development with the preceding cohorts (the National Child Development Study, held at the UK Data Archive under GN 33004, and the 1970 Birth Cohort Study, held under GN 33229)
• to collect information on previously neglected topics, such as fathers' involvement in children's care and development
• to focus on parents as the most immediate elements of the children's 'background', charting their experience as mothers and fathers of newborn babies in the year 2000, recording how they (and any other children in the family) adapted to the newcomer, and what their aspirations for her/his future may be
• to emphasise intergenerational links including those back to the parents' own childhood
• to investigate the wider social ecology of the family, including social networks, civic engagement and community facilities and services, splicing in geo-coded data when available

Additional objectives subsequently included for MCS were:

• to provide control cases for the national evaluation of Sure Start (a government programme intended to alleviate child poverty and social exclusion)
• to provide samples of adequate size to analyse and compare the smaller countries of the United Kingdom, and include disadvantaged areas of England

Further information about the MCS can be found on the Centre for Longitudinal Studies web pages.

The content of MCS studies, including questions, topics and variables can be explored via the CLOSER Discovery website.

The first sweep (MCS1) interviewed both mothers and (where resident) fathers (or father-figures) of infants included in the sample when the babies were nine months old, and the second sweep (MCS2) was carried out with the same respondents when the children were three years of age. The third sweep (MCS3) was conducted in 2006, when the children were aged five years old, the fourth sweep (MCS4) in 2008, when they were seven years old, the fifth sweep (MCS5) in 2012-2013, when they were eleven years old, the sixth sweep (MCS6) in 2015, when they were fourteen years old, and the seventh sweep (MCS7) in 2018, when they were seventeen years old. End User Licence versions of MCS studies:
The End User Licence (EUL) versions of MCS1, MCS2, MCS3, MCS4, MCS5, MCS6 and MCS7 are held under UK Data Archive SNs 4683, 5350, 5795, 6411, 7464, 8156 and 8682 respectively. The longitudinal family file is held under SN 8172.

Sub-sample studies:
Some studies based on sub-samples of MCS have also been conducted, including a study of MCS respondent mothers who had received assisted fertility treatment, conducted in 2003 (see EUL SN 5559). Also, birth registration and maternity hospital episodes for the MCS respondents are held as a separate dataset (see EUL SN 5614).


How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from MCS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Secure Access datasets:
Secure Access versions of the MCS have more restrictive access conditions than versions available under the standard End User Licence or Special Licence (see 'Access data' tab above).

Secure Access versions of the MCS include:

• detailed sensitive variables not available under EUL. These have been grouped thematically and are held under SN 8753 (socio-economic, accommodation and occupational data), SN 8754 (self-reported health, behaviour and fertility), SN 8755 (demographics, language and religion) and SN 8756 (exact participation dates). These files replace previously available studies held under SNs 8456 and 8622-8627
  
• detailed geographical identifier files which are grouped by sweep held under SN 7758 (MCS1), SN 7759 (MCS2), SN 7760 (MCS3), SN 7761 (MCS4), SN 7762 (MCS5 2001 Census Boundaries), SN 7763 (MCS5 2011 Census Boundaries), SN 8231 (MCS6 2001 Census Boundaries), SN 8232 (MCS6 2011 Census Boundaries), SN 8757 (MCS7), SN 8758 (MCS7 2001 Census Boundaries) and SN 8759 (MCS7 2011 Census Boundaries). These files replace previously available files grouped by geography SN 7049 (Ward level), SN 7050 (Lower Super Output Area level), and SN 7051 (Output Area level)
• linked education administrative datasets for Key Stages 1, 2, 4 and 5 held under SN 8481 (England). This replaces previously available datasets for Key Stage 1 (SN 6862) and Key Stage 2 (SN 7712)
  
• linked education administrative datasets for Key Stage 1 held under SN 7414 (Scotland)
• linked education administrative dataset for Key Stages 1, 2, 3 and 4 under SN 9085 (Wales)
  
• linked NHS Patient Episode Database for Wales (PEDW) for MCS1 – MCS5 held under SN 8302
• linked Scottish Medical Records data held under SNs 8709, 8710, 8711, 8712, 8713 and 8714;
• Banded Distances to English Grammar Schools for MCS5 held under SN 8394
• linked Health Administrative Datasets (Hospital Episode Statistics) for England for years 2000-2019 held under SN 9030
• linked Health Administrative Datasets (SAIL) for Wales held under SN 9310
  
• linked Hospital of Birth data held under SN 5724.

The linked education administrative datasets held under SNs 8481,7414 and 9085 may be ordered alongside the MCS detailed geographical identifier files only if sufficient justification is provided in the application.

Researchers applying for access to the Secure Access MCS datasets should indicate on their ESRC Accredited Researcher application form the EUL dataset(s) that they also wish to access (selected from the MCS Series Access web page).

The Millennium Cohort Study: Linked Health Administrative Data (Scottish Medical Records), Inpatient and Day Care Attendance, 2000-2015: Secure Access includes data files from the NHS Digital Hospital Episode Statistics database for those cohort members who provided consent to health data linkage in the Age 50 sweep, and had ever lived in Scotland. The Scottish Medical Records database contains information about all hospital admissions in Scotland. This study concerns the Scottish Birth Records.

Other datasets are available from the Scottish Medical Records database, these include:

• Child Health Reviews (CHR) held under SN 8709
• Prescribing Information System (PIS) held under SN 8710
• Scottish Immunisation and Recall System (SIRS) held under SN 8711
• Scottish Birth Records (SMR11) held under SN 8712
• Outpatient Attendance (SMR00) held under <a href="https://beta.ukdataservice.ac.uk/datacatalogue/studies/study?id=8714"

Abstract (en): The National Hospital Ambulatory Medical Care Survey (NHAMCS) was inaugurated in 1992 to fill a gap in data about ambulatory medical care in the United States. Although the National Ambulatory Medical Care Survey (NAMCS) collects annual data on patient visits to physician offices, it excludes the hospital emergency room and outpatient department visits that make up a large part of the total ambulatory care received each year. The NHAMCS provides data from samples of patient records selected from emergency departments (EDs) and outpatient departments (OPDs) of a national sample of hospitals. The resulting national estimates describe the use of hospital ambulatory medical care services in the United States. For the 1996 survey, data were collected from 235 OPDs and 392 EDs. Among the variables included are age, race, and sex of the patient, reason for the visit, physician's diagnoses, cause of injury (ED only), surgical procedures (OPD only), medication therapy, and expected source of payment. 2006-01-18 File CB2365.ALL was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads. (1) This collection has not been processed by ICPSR staff. ICPSR is distributing the data and documentation for this collection in essentially the same form in which they were received. When appropriate, hardcopy documentation has been converted to machine-readable form and variables have been recoded to ensure respondents' anonymity. (2) Per agreement with NCHS, ICPSR distributes the data file(s) and technical documentation in this collection in their original form as prepared by NCHS.

The National Hospital Ambulatory Medical Care Survey (NHAMCS) was inaugurated in 1992 to fill a gap in data about ambulatory medical care in the United States. Although the National Ambulatory Medical Care Survey (NAMCS) collects annual data on patient visits to physician offices, it excludes the hospital emergency room and outpatient department visits that make up a large part of the total ambulatory care received each year. The 1992 NHAMCS provides data from samples of patient records selected from emergency departments (EDs) and outpatient departments (OPDs) of a national sample of hospitals. The resulting national estimates describe the use of hospital ambulatory medical care services in the United States. Between December 2, 1991, and December 27, 1992, data were collected from 314 OPDs and 437 EDs. Among the variables included are age, race, and sex of the patient, along with the reason for the visit, physician's diagnoses, cause of injury (ED only), surgical procedures (OPD only), medication therapy, and expected source of payment.

The National Ambulatory Medical Care Survey (NAMCS) provides data from samples of patient records selected from a national sample of office-based physicians. These national estimates describe the utilization of ambulatory medical care services in the United States. In 1991, there were 33,795 patient records provided by 1,354 doctors who participated in the survey. The survey obtains information on the age, race, and sex of the patient, and on physician characteristics such as geographic location and specialization. Data describing the nature of the office visit include the expected source of payment, patient's problem, prior visit status, referral status, physician's diagnoses, diagnostic and therapeutic services provided, and disposition and duration of the visit. Other variables cover drugs/medications ordered, administered, or provided during office visits, such as medication code, generic name and code, brand name, entry status, prescription status, composition status, and related ingredient codes.

Market Overview:

The global electronic medical records market size reached USD 35.1 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 51.6 Billion by 2033, exhibiting a growth rate (CAGR) of 4.15% during 2025-2033.

Report Attribute	Key Statistics
Base Year	2024
Forecast Years	2025-2033
Historical Years	2019-2024
Market Size in 2024	USD 35.1 Billion
Market Forecast in 2033	USD 51.6 Billion
Market Growth Rate 2025-2033	4.15%

Electronic medical records (EMRs) refer to digital records that consist of information regarding the patient’s health. It includes patient demographics, medical history, medications, allergies, radiology reports, immunization status, laboratory test results, vital signs and billing information. EMRs can be deployed through cloud computing and on-premises software. Cloud-based solutions enable centralized data storage and online access across multiple geographical locations and on-premises solutions are utilized for local computing requirements. These systematic records aid in tracking and monitoring patients, identifying patterns and improving the quality of healthcare being offered. They can also enhance communication and productivity between healthcare providers and patients, thereby improving health outcomes and patient safety.

https://www.imarcgroup.com/CKEditor/3606274c-7e23-493d-a29c-8efc2ff36e68electronic-medical-records-market-global-overview-template.webp" style="height:450px; width:800px" />

Electronic Medical Records Market Trends:

Increasing digitization, along with the significant growth in the healthcare information technology (IT) industry across the globe, is one of the key factors creating a positive outlook for the market. Furthermore, the rising prevalence of chronic medical ailments and the growing geriatric population that is more prone to such problems, are driving the market. Consequently, there has been an increasing adoption of patient-centric EMR systems to facilitate the patient's direct involvement throughout the documentation process. Additionally, various technological advancements, such as the advent of cloud-based EMR solutions, are acting as another growth-inducing factor. These solutions provide quality care to the patients and enhanced protection from data disruption caused by any accidents or mishaps. Other factors, including improving healthcare infrastructure and the implementation of favorable population health management programs, are expected to drive the market further.

Key Market Segmentation:

IMARC Group provides an analysis of the key trends in each segment of the global electronic medical records market report, along with forecasts at the global, regional and country levels from 2025-2033. Our report has categorized the market based on type, component, functionality, deployment type, application and end user.

Breakup by Type:

https://www.imarcgroup.com/CKEditor/20622401-0f06-4087-931c-892f5a045937electronic-medical-records-market-segments-template.webp" style="height:450px; width:800px" />

• Traditional EMRs
• Speech Enabled EMRs
• Interoperable EMRs
• Others
  

Breakup by Component:

• Hardware
• Software
• Services
  

Breakup by Functionality:

• Basic Systems
• Fully Functional Systems
  

Breakup by Deployment Type:

• Cloud-based
• On-premises
  

Breakup by Application:

• Specialty Based
  • Cardiology
  • Neurology
  • Radiology
  • Oncology
  • Others
• General Applications
  

Breakup by End User:

• Hospitals and Clinics
• Specialty Centers
• Others
  

Breakup by Region:

https://www.imarcgroup.com/CKEditor/00ccfea2-116a-4746-ae7e-b90f98283bf9electronic-medical-records-market-global-region-template.webp" style="height:450px; width:800px" />

• North America
  • United States
  • Canada
• Asia Pacific
  • China
  • Japan
  • India
  • South Korea
  • Australia
  • Indonesia
  • Others
• Europe
  • Germany
  • France
  • United Kingdom
  • Italy
  • Spain
  • Russia
  • Others
• Latin America
  • Brazil
  • Mexico
  • Others
• Middle East and Africa
  

Competitive Landscape:

The report has also analysed the competitive landscape of the market with some of the key players being AdvancedMD Inc. (Global Payments Inc.), Veradigm LLC, Oracle Corporation, CureMD Healthcare, eClinicalWorks, Epic Systems Corporation, General Electric Company, Greenway Health LLC, McKesson Corporation, Modernizing Medicine Inc., Nextgen Healthcare Inc., etc.

Report Coverage:

<td

Report Features	Details
Base Year of the Analysis	2024
Historical Period	2019-2024
Forecast Period	2025-2033
Units	Billion USD
Segment Coverage	Type, Component, Functionality, Deployment Type, Application, End User, Region
Region Covered	Asia Pacific, Europe, North America, Latin America, Middle East and Africa
Countries Covered	United States, Canada, Germany, France, United Kingdom, Italy, Spain, Russia, China, Japan, India, South Korea, Australia, Indonesia, Brazil, Mexico
Companies Covered	AdvancedMD Inc. (Global Payments Inc.), Veradigm LLC, Oracle Corporation, CureMD Healthcare, eClinicalWorks, Epic Systems Corporation, General Electric Company, Greenway Health LLC, McKesson Corporation, Modernizing Medicine Inc. and Nextgen Healthcare Inc.
Customization Scope	10% Free Customization
Post-Sale Analyst Support	10-12 Weeks

The Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) is the largest publicly available all-payer inpatient care database in the United States. The NIS is designed to produce U.S. regional and national estimates of inpatient utilization, access, cost, quality, and outcomes. Unweighted, it contains data from more than 7 million hospital stays each year. Weighted, it estimates more than 35 million hospitalizations nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels. Starting with the 2012 data year, the NIS is a sample of discharges from all hospitals participating in HCUP, covering more than 97 percent of the U.S. population. For prior years, the NIS was a sample of hospitals. The NIS allows for weighted national estimates to identify, track, and analyze national trends in health care utilization, access, charges, quality, and outcomes. The NIS's large sample size enables analyses of rare conditions, such as congenital anomalies; uncommon treatments, such as organ transplantation; and special patient populations, such as the uninsured. NIS data are available since 1988, allowing analysis of trends over time. The NIS inpatient data include clinical and resource use information typically available from discharge abstracts with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). Data elements include but are not limited to: diagnoses, procedures, discharge status, patient demographics (e.g., sex, age), total charges, length of stay, and expected payment source, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. The NIS excludes data elements that could directly or indirectly identify individuals. Restricted access data files are available with a data use agreement and brief online security training.

ABSTRACT Objectives: to analyze the main non-conformities of the nursing records of a public hospital in Natal, Brazil. Methods: this is a descriptive, cross-sectional study, with a quantitative approach. This study was conducted in nursing departments of medical and surgical wards. The sample was composed of 120 medical records of inpatients between October and December 2016. The obtained data were tabulated and analyzed by simple statistics in absolute and relative frequency using the 2013 Microsoft Excel software. The Pareto Diagram was used to evaluate the non-conformities of the records. Results: the main problems in the nursing records were the absence of the professional category and the nursing council number, responsible for 41.8% of the non-conformities in the records of nursing technicians; for nurses’ records, the main non-conformities were the absence of time and the illegible handwriting, with 61.2%. Conclusions: the study showed that nursing professionals perform their records incompletely and often do not document the care provided.

EHR-RelB is a benchmark dataset for biomedical concept relatedness, consisting of 3630 concept pairs sampled from electronic health records (EHRs). EHR-RelA is a smaller dataset of 111 concept pairs, which are mainly unrelated.

Our Future Health is a prospective, observational cohort study of the general adult population of the United Kingdom (UK). The programme aims to support a wide range of observational health research. We gather personal, health and lifestyle information from each participant through a self-completed baseline health questionnaire and at an in-person clinic visit. We will further link this data to other health-related data sets. Participants have also given consent for us to recontact them, for example to invite them to take part in further or repeat data collections, or other embedded studies such as clinical trials.

The Our Future Health programme is currently open to all adults (18 years and older) living in the UK. In July 2022, we started recruiting participants in England and will continue to expand across the rest of the UK. The data we’ve gathered so far (June 2025) includes linked NHS England clinical data on 1,527,723 participants

Additional linked datasets are available: - ‘Baseline Health Questionnaire Data’ which contains baseline demographic information and responses to our health questionnaire from 1,781,891 participants. - ‘Genotype Array Data’ which includes genotype array data on 707,522 variants from a subset of 650,979 participants - Clinical Measurements Data which contains clinical data from 1,324,884 participants.

The data is stored in the Our Future Health Trusted Research Environment. We de-identify all participant data we gather before it’s available for use. All researchers will need to become registered researchers at Our Future Health and have an approved research study before they're given access to the data.

We aim to collect a variety of data types from up to 5 million adult participants from across the UK. We hope to make more data types available on a quarterly basis.